Big Data Research Outcomes He waka eke noa Better Together Research excellence for healthy tamariki A Better Start is funded by the Ministry of Business, Innovation and Employment

Our Team Barry Taylor – Principal Investigator (DSM) Rick Audas – Theme co-lead (DSM) Justine Camp, Nick Bowden (DSM) Rose Richards, Jesse Kokaua (Centre for Pacific Health, Va’a o Tautai) Barry Milne – Theme co-lead (COMPASS) Nichola Shackleton, Sheree Gibb, Jessica McLay, Stephanie D’Souza (COMPASS)

SUCCESSFUL LEARNING AND LITERACY Big Data’s role Work with the three themes to assess time trends, spatial distribution, and answer other key questions using whole population data (IDI) Healthy weight - B4 School Check Resilient Teens - Pharmaceutical data Successful Learning and Literacy - B4 School Check, School data HEALTHY WEIGHT RESILIENT TEENS SUCCESSFUL LEARNING AND LITERACY BIG DATA

Integrated Data Infrastructure (IDI) DISCLAIMER Access to the data presented was managed by Statistics New Zealand under strict micro-data access protocols and in accordance with the security and confidentiality provisions of the Statistic Act 1975. Our findings are not Official Statistics. The opinions, findings, recommendations, and conclusions expressed are those of the researchers, not Statistics NZ, or the University of Auckland.

B4 School Check: Who misses out? Established September 2008 (we use 2010/2011 to 2015/2016) Eligible children are those who are enrolled with a PHO on their 4th birthday Coverage between 72-92%

B4 School Check: Who misses out? Vision and Hearing Tests Nurse Checks Growth Dental Immunisation Parents Evaluation of Developmental Status (PEDS) Strengths and Difficulties Questionnaire (SDQ) ECE Teacher using data from two nationally representative health surveys, the 1988–2010 National Health and Nutrition Examination Surveys and the 2003–2011 National Survey of Children’s Health 91,000-100,000 NHANES 5000 per year

B4 School Check: Who misses out? Ethnicity The Lancet 2002 360, 473-482DOI: (10.1016/S0140-6736(02)09678-2)

B4 School Check: Who misses out? Deprivation The Lancet 2002 360, 473-482DOI: (10.1016/S0140-6736(02)09678-2)

B4 School Check: Who misses out? Children most in need may miss referrals for programs and interventions that could increase their readiness to enter school A potential reinforcing of existing inequalities More effort needed to ensure that those with the greatest need get access to health services, programmes and interventions

Healthy weight: Community level trends

Healthy weight: Community level trends Prevalence Slope https://compassnz.shinyapps.io/BetterStart/  

Healthy weight: Community level trends Do levels of childhood obesity vary between communities in NZ? We found considerable community-level differences in obesity prevalence, ranging from less than 10% to over 25%. Do trends in childhood obesity in 2010/11 to 2015/16 vary between communities? the pattern of change over time was similar across communities, with the majority of communities experiencing a similar rate of decline in obesity. To what extent can variations in obesity between communities be explained by differences in socioeconomic deprivation, ethnic composition and urbanicity? More than a third of the variation in community obesity prevalence could be explained by the socioeconomic and urban composition of the community, with around a further 10% explained by ethnic composition of communities

Resilient teens: Antidepressant trends Bowden et al., under consideration Journal of Child Psychology and Psychiatry Bowden et al., under consideration

Resilient teens: Antidepressant trends Bowden et al., under consideration Journal of Child Psychology and Psychiatry

Successful learning and literacy: Pacific Literacy Interventions

Successful learning and literacy: Pacific Literacy Interventions Some demographic factors do predict literacy intervention among Pacific children But… predictive ability of B4 School Check data is poor (AUC = 0.592)

Big Data summary Developed research projects with each of the themes which make use of whole population (IDI) data Other projects Antibiotics and obesity Trends in ADHD medication dispensing Use of whole population data to identify cases of Autism Spectrum Disorder (ASD) Caesarean births and child education outcomes Exploring more possibilities using family links